Auto-CoT generates diverse LLM reasoning demos by clustering questions

Auto-CoT is a method for automatically generating few-shot chain-of-thought demonstrations for large language models. Instead of manually crafting examples, Auto-CoT clusters questions from a pool and selects one representative from each cluster to ensure diversity. The language model then generates the step-by-step reasoning for these representative questions, creating a set of diverse demonstrations. This approach aims to improve model reasoning by avoiding the pitfalls of similarity-based sampling, which can amplify errors, and by eliminating the labor-intensive process of manual example creation. AI

IMPACT Automates the creation of diverse reasoning examples for LLMs, potentially improving performance and reducing manual effort.

RANK_REASON The item describes a novel method for generating training data for LLMs, which is a research contribution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• Auto-CoT
• Lloyd's algorithm
• Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

• model release

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